Machine Learning Based Bot Detection on X With Temporal and Semantic Feature Integration

Escalating proliferation of inorganic accounts, commonly known as bots, within the digital ecosystem represents an ongoing and multifaceted challenge to online security, trustworthiness, and user experience. These bots, often employed for the dissemination of malicious propaganda and manipulation of public opinion, wield significant influence in social media spheres with far-reaching implications for electoral processes, political campaigns, and international conflicts. Swift and accurate identification of inorganic accounts is of paramount importance in mitigating their detrimental effects. This research article focuses on the identification of such accounts and explores various effective methods for their detection through machine learning techniques. In response to the pervasive presence of bots in the contemporary digital landscape, this study extracts temporal and semantic features from tweet behaviors and proposes a bot detection framework using fundamental machine learning approaches, including support vector machines (SVMs) and k-means clustering. Furthermore, the research ranks the importance of these extracted features for each detection technique and also provides uncertainty quantification using a distribution-free method, called the conformal prediction, thereby contributing to the development of effective strategies for combating the prevalence of inorganic accounts in social media platforms.